Studying the visual system (1) Early Vision and The visual system - PowerPoint PPT Presentation

Studying the visual system (1) Early Vision and The visual system can be (and is) studied using many different techniques. In this course we will consider: Visual System Development Psychophysics What is the level of human visual performance under various different conditions? Dr. James A. Bednar Anatomy Where are the visual system parts located, and jbednar@inf.ed.ac.uk what do they look like? http://homepages.inf.ed.ac.uk/jbednar Gross anatomy What do the visual system organs and tissues look like, and how are they connected? Histology What cellular and subcellular structures can be seen under a microscope? CNV Spring 2015: Vision background 1 CNV Spring 2015: Vision background 2 Studying the visual system (2) Electromagnetic spectrum Physiology What is the behavior of the component parts of the visual system? Electrophysiology What is the electrical behavior of neurons, measured with an electrode? (From web) Imaging What is the behavior of a large area of the nervous system? Genetics Which genes control visual system Start with the physics: visible portion is small, but provides development and function, and what do they do? much information about biologically relevant stimuli CNV Spring 2015: Vision background 3 CNV Spring 2015: Vision background 4

The visible range may be special Cone spectral sensitivities (Nave 2014; hyperphysics.phy-astr.gsu.edu) Possible explanation: • Animals evolved in water • Water is transparent to a narrow range of wavelengths... (Dowling, 1987) • ... that also happens to be the peak of the sun’s Somehow we make do with sampling the visible range of radiation wavelengths at only three points (3 cone types) CNV Spring 2015: Vision background 5 CNV Spring 2015: Vision background 6 Early visual pathways Higher areas • Many higher areas beyond V1 • Selective for � 1994 L. Kibiuk faces, motion, etc. • Often c multisensory Eye LGN V1 • Not as well Signals travel from retina, to LGN, Macaque monkey visual areas understood (Van Essen et al. 1992) then to primary visual cortex CNV Spring 2015: Vision background 7 CNV Spring 2015: Vision background 8

Circuit Image formation diagram (Kandel et al. 1991) Connections between macaque monkey visual areas (Van Essen et al. 1992) A bit messy! Fixed Adjustable Sampling (Yet still just a start.) Camera: lens shape focal length uniform Eye: focal length lens shape higher at fovea CNV Spring 2015: Vision background 9 CNV Spring 2015: Vision background 10 Visual fields Retinotopic map Right eye right Mapping of Right LGN CMVC figure 2.1 visual field in Primary macaque Visual field visual cortex monkey Optic Left LGN (V1) chiasm left Blasdel and Left eye Campbell 2001 • Each eye sees partially overlapping areas • Visual field is mapped onto cortical surface • Inputs from opposite hemifield cross over at chiasm • Fovea is overrepresented CNV Spring 2015: Vision background 11 CNV Spring 2015: Vision background 12

Effect of foveation Retinal surface (Ahnelt & Kolb 2000); no scale in original (From omni.isr.ist.utl.pt) Fovea (center ❀ ) Periphery • Fovea: densely packed L,M cones (no rods) Smaller, tightly packed cones in the fovea • No S cones in central fovea; sparse elsewhere give much higher resolution • Cones are larger in periphery ( ∗ : S-cones) • Cone spacing also increases, with gaps filled by rods CNV Spring 2015: Vision background 13 CNV Spring 2015: Vision background 14 Retinal circuits LGN layers Macaque; Hubel & Wiesel 1977 (Kandel et al. 1991) Multiple aligned representations of visual field in the LGN Rod pathway Rod, rod bipolar cell, ganglion cell for different eyes and cell types Cone pathway Cone, bipolar cell, ganglion cell CNV Spring 2015: Vision background 15 CNV Spring 2015: Vision background 16

Cortical layers V1 layers Macaque V1, webvision.umh.es Mouse S1 (Boyle et al. 2011) 500 µm 200 µm Same as previous slide, but for macaque V1 Each layer labeled separately, with Brodmann numbering CNV Spring 2015: Vision background 17 CNV Spring 2015: Vision background 18 Retinal/LGN cell response types Color-opponent retinal/LGN cells (From webexhibits.org) Types of receptive fields based on responses to light: Red/Green cells: (+R,-G), (-R,+G), (+G,-R), (-G,+R) in center in surround Blue/Yellow cells: (+B,-Y); others? coextensive? On-center excited inhibited Error: light arrows in the figure are backwards! Actual Off-center inhibited excited organization mostly consistent with random wiring CNV Spring 2015: Vision background 19 CNV Spring 2015: Vision background 20

V1 simple cell responses V1 complex cell responses 2-lobe simple 3-lobe simple (Approximately same response to all these patterns) cell cell Complex cells are also orientation selective, but have Starting in V1, only oriented patterns will cause any responses (relatively) invariant to phase significant response Cannot measure complex RFs using pixel-based Simple cells: pattern preferences can be plotted as above correlations CNV Spring 2015: Vision background 21 CNV Spring 2015: Vision background 22 Spatiotemporal receptive fields Contrast perception 0% 3% 6% 12% 25% 100% • Neurons are selective for multiple stimulus dimensions at once • Humans can detect patterns over a huge contrast range • Typically prefer lines moving in direction perpendicular to • In the laboratory, increasing contrast above a fairly low value does not aid detection orientation preference • See 2AFC (two-alternative forced-choice) test in (Cat V1; DeAngelis et al. 1999) google and ROC (Receiver Operating Characteristic) in Wikipedia for more info on how such tests work CNV Spring 2015: Vision background 23 CNV Spring 2015: Vision background 24

Contrast-invariant tuning Definitions of contrast Luminance: Physical amount of light • Single-cell tuning curves are typically Gaussian Contrast: Luminance relative to background levels • 5%, 20%, 80% contrasts Contrast is a fuzzy concept, because “background” is not shown well defined. Clear only in special cases: • Peak response increases, but Weber contrast (e.g. a tiny spot on uniform background) • Tuning width changes little C = Lmax − Lmin Lmin • Contrast where peak is Michelson contrast (e.g. a full-field sine grating): reached varies by cell Lmax − Lmin (Sclar & Freeman 1982) C = Lmax − Lmin 2 Lmax + Lmin = Lavg CNV Spring 2015: Vision background 25 CNV Spring 2015: Vision background 26 Measuring cortical maps Retinotopy/orientation map o o o o o o o o o o 0 0 2 4 6 8 2 4 6 8 Tree shrew; Bosking et al. 2002; 2 × 2mm o 2 o CMVC figure 2.3 2 o o 4 4 o 6 o 6 • Surface reflectance (or voltage-sensitive-dye o 8 emission) changes with activity • Measured with optical imaging, e.g. using a CCD • Tree shrew has no fovea ❀ isotropic map • Preferences computed as correlation between • All orientations represented for each retina location measurement and input • Orientation map is smooth, with local patches CNV Spring 2015: Vision background 27 CNV Spring 2015: Vision background 28

Macaque V1 orientation map V1 ocular dominance map Macaque; Blasdel 1992; 4 × 3mm Macaque; Blasdel 1992; 4 × 3mm • Macaque monkey has fovea but similar orientation map • Most neurons are binocular, but prefer one eye • Retinotopic map (not measured) highly nonlinear • Eye preference alternates in stripes or patches CNV Spring 2015: Vision background 29 CNV Spring 2015: Vision background 30 Combined OR/OD map in V1 Direction map in ferret V1 Macaque; Blasdel 1992; 4 × 3mm (Adult ferret; Weliky et al. 1996) OR/Direction pref. Direction preference • Same neurons have preference for both features (1 × 1.4mm) (3.2 × 2mm) • OR has linear zones, fractures, pinwheels, saddles • Local patches prefer different directions • OD boundaries typically align with linear zones • Single-OR patches often subdivided by direction • Other maps: spatial frequency, color, disparity CNV Spring 2015: Vision background 31 CNV Spring 2015: Vision background 32

Cell-level organization Cell-level organization 2 • Individual cells can be tagged with feature Two-photon microscopy: preference • Newer technique with • In rat, orientation cell-level resolution preferences are random • Can measure a small • Random also expected in volume very precisely mouse, squirrel (Ohki et al. 2005) (Ohki et al. 2005) Rat V1 (scale bars 0.1mm) Rat V1 (scale bars 0.1mm) CNV Spring 2015: Vision background 33 CNV Spring 2015: Vision background 34 Cell-level organization 3 Cell-level organization 4 • Very close match with optical imaging results • In cat, validates results from optical imaging • Stacking labeled cells from all layers shows very strong • Smooth organization for Low-res map (2 × 1.2mm) ordering spatially and in direction overall across layers • Sharp, well-segregated • Selectivity in pinwheels discontinuities controversial; apparently (Ohki et al. 2005) lower Stack of all labeled cells (0.6 × 0.4mm) Cat V1 Dir. (scale bars 0.1mm) (Ohki et al. 2006) CNV Spring 2015: Vision background 35 CNV Spring 2015: Vision background 36